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arXiv Machine Learning By Tong Chen, Raghavendra Selvan

A Discrepancy-Based Perspective on Dataset Condensation

Read the original on arXiv Machine Learning →

The paper introduces a unified framework for dataset condensation (DC) that generalizes existing methods by using discrepancy measures to quantify the distance between probability distributions. It extends the traditional goal of DC—creating a small synthetic dataset that preserves generalization—to include additional objectives such as robustness and privacy. The framework positions DC as a formal approximation problem, broadening its applicability across different machine learning regimes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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